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proofite

by com.proofite

Rate one story

rate_briefing_entry

Give a precise up/down signal on one story of a briefing: more like this, less like this, more technical, simpler, deeper, shorter, follow it, or mute its topics. Applies to the topics of that story, so the next briefing weighs them differently. A story usually has several topics: pass topics to hit only some of them (e.g. "less" of "demographics" but not of "japan"). Sending the same signal again with different topics REPLACES the previous one: the topics you drop are restored, nothing is double-counted.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
signalYesThe signal to record
topicsNoOnly these topics of the story (as listed by get_briefing). Omit to apply to all of its topics.
entry_idYesEntry id from get_briefing
briefing_idNoWhich briefing. Omit for the latest.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed1 schema field changed
    • addedInput schema / properties / topics
      Added value: +{
+  "description": "Only these topics of the story (as listed by get_briefing). Omit to apply to all of its topics.",
+  "items": {
+    "type": "string"
+  },
+  "maxItems": 5,
+  "type": "array"
+}
  2. First observed

TDQS

A4.5/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

It discloses non-obvious stateful behavior beyond the sparse annotations: signals apply to topics, future briefings weigh them differently, re-sending with different topics replaces the previous signal, dropped topics are restored, and nothing is double-counted. This is especially valuable for a mutating tool with no idempotence guarantee.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Three dense sentences with no filler. The main action and examples come first, while the replacement edge case is saved for last. Every sentence contributes distinct, necessary information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a mutation tool with no output schema, the description covers the target entry, optional briefing, topic scoping, and side effects on future briefings. An agent has enough to call it correctly and predict the outcome.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the baseline is 3. The description adds meaning the schema cannot convey: that a story usually has several topics, that `topics` can narrow the signal to a subset, and that repeating the call with different topics replaces the old signal rather than accumulating.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with a precise verb and resource: 'Give a precise up/down signal on one story of a briefing.' It enumerates the possible signals and scopes the effect to the story's topics, which clearly distinguishes it from broader preference tools like tune_topic.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies the tool is for adjusting future briefings based on a specific story and explains how to restrict to certain topics. However, it does not explicitly say when to prefer it over overlapping sibling tools such as tune_topic or set_learned_profile, nor does it give exclusions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

A4.2/5.0
Disambiguation4/5

Most tools target distinct operations (e.g., add_feed vs. remove_feed, get_source vs. list_sources). However, there is some ambiguity between correct_briefing and tune_topic (both adjust content based on user feedback) and between request_deep_dive and correct_briefing (both can trigger deeper investigation). The descriptions help, but an agent could misselect.

Naming Consistency5/5

Tool names follow a very consistent verb_noun pattern (e.g., add_feed, create_source, generate_briefing, list_sources). All use snake_case with clear, descriptive verbs and nouns. No mixing of conventions.

Tool Count4/5

With 27 tools, the server covers a rich domain of news feed management, briefing generation, and user preferences. This is slightly above the typical sweet spot but still reasonable given the complexity. Each tool serves a distinct purpose; the count is justified.

Completeness5/5

The tool set provides full lifecycle coverage: sources can be created, read, updated, and deleted; feeds and searches can be added and removed; briefings can be generated, listed, read, rated, and corrected; deep dives and read-later items are supported. There are no obvious gaps—the domain is thoroughly covered.

Resources